Please use this identifier to cite or link to this item: https://doi.org/10.1136/amiajnl-2012-001336
Title: Automatic glaucoma diagnosis through medical imaging informatics
Authors: Liu, J.
Zhang, Z.
Wong, D.W.K.
Xu, Y.
Yin, F.
Cheng, J.
Tan, N.M.
Kwoh, C.K.
Xu, D.
Tham, Y.C.
Aung, T.
Wong, T.Y. 
Issue Date: 2013
Citation: Liu, J., Zhang, Z., Wong, D.W.K., Xu, Y., Yin, F., Cheng, J., Tan, N.M., Kwoh, C.K., Xu, D., Tham, Y.C., Aung, T., Wong, T.Y. (2013). Automatic glaucoma diagnosis through medical imaging informatics. Journal of the American Medical Informatics Association 20 (6) : 1021-1027. ScholarBank@NUS Repository. https://doi.org/10.1136/amiajnl-2012-001336
Abstract: Background Computer-aided diagnosis for screening utilizes computer-based analytical methodologies to process patient information. Glaucoma is the leading irreversible cause of blindness. Due to the lack of an effective and standard screening practice, more than 50% of the cases are undiagnosed, which prevents the early treatment of the disease. Objective To design an automatic glaucoma diagnosis architecture automatic glaucoma diagnosis through medical imaging informatics (AGLAIA-MII) that combines patient personal data, medical retinal fundus image, and patient's genome information for screening. Materials and methods 2258 cases from a population study were used to evaluate the screening software. These cases were attributed with patient personal data, retinal images and quality controlled genome data. Utilizing the multiple kernel learningbased classifier, AGLAIA-MII, combined patient personal data, major image features, and important genome single nucleotide polymorphism (SNP) features. Results and discussion Receiver operating characteristic curves were plotted to compare AGLAIAMII's performance with classifiers using patient personal data, images, and genome SNP separately. AGLAIA-MII was able to achieve an area under curve value of 0.866, better than 0.551, 0.722 and 0.810 by the individual personal data, image and genome information components, respectively. AGLAIA-MII also demonstrated a substantial improvement over the current glaucoma screening approach based on intraocular pressure. Conclusions AGLAIA-MII demonstrates for the first time the capability of integrating patients' personal data, medical retinal image and genome information for automatic glaucoma diagnosis and screening in a large dataset from a population study. It paves the way for a holistic approach for automatic objective glaucoma diagnosis and screening.
Source Title: Journal of the American Medical Informatics Association
URI: http://scholarbank.nus.edu.sg/handle/10635/109211
ISSN: 10675027
DOI: 10.1136/amiajnl-2012-001336
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